Using real-time acceleration data for exercise movement training with a decision tree approach

In this paper, a movement-training system aiming to classify motions for physical education is proposed and analyzed. Traditional physical education requires an instructor teaching exercise movement individually. Teaching every student in a big class demands considerable time and efforts. Utilization of computer-assisted instruction (CAI) becomes pervasive in e-learning trend. However, CAI is often confined in literal form course such as mathematics and language courses. It is necessary to develop a motion-training system for physical education. In this paper, we develop a low-cost motion capture with Wii Remote Control (Wiimote) for training movement exercise, such as tennis and baseball. This system applies Wiimotes to capture acceleration of each part of limbs. Each Wiimote is attached to the limb which then sends back the acceleration information to the computer via Bluetooth wireless link. After gathering the acceleration data of multiple limbs’ parts, the computer recognizes the motion and classifies the motion to several correct and incorrect categories. As a result, it is able to provide the appropriate advice to the students. The system applies a modified ID3 inductive learning to generate a decision tree with continuous-valued attributes. We develop an easy-to-use GUI interface for coaches. The results show that the average accuracy of classification is 83%. The system reduces the workload of the coach and improves teaching and learning performance.